Neuropharmacology
December 27, 2022
Robin Carhart‐Harris, Shamil Chandaria, David Erritzøe et al.
106 citations
A theoretical model proposes that psychopathology arises from a defensive process called canalization, which narrows an individual's range of thoughts, feelings, and behaviors by increasing precision or reducing variance in neural responses. This contrasts with an early form of plasticity, TEMP (Temperature or Entropy Mediated Plasticity), which increases variance and learning rate. Canalization entrenches pathology as the agent develops expertise in their disorder, while TEMP, combined with gentle psychological support, may counter this entrenchment. The model distinguishes adaptive from maladaptive canalization and suggests concrete experiments to test its hypotheses.
Neuroscience and biobehavioral reviews
September 1, 2025
Ruben Laukkonen, Karl Friston, Shamil Chandaria
17 citations
A theoretical paper proposes that active inference can model consciousness through three conditions: a world model (epistemic field) defining what can be known, inferential competition (Bayesian binding) selecting only coherent inferences that reduce long-term uncertainty, and epistemic depth—a recursive sharing of beliefs throughout a hierarchical system like the brain. This loop allows the world model to know itself non-locally and continuously evidence that knowing, distinct from self-consciousness. The authors formally propose a hyper-model for precision-control whose latent states encode global weighting rules, enacting epistemic agency and flexibility reminiscent of general intelligence. The theory also addresses altered states, meditation, and the full spectrum of conscious experience.
Artificial General Intelligence
January 1, 2026
Ruben E. Laukkonen, Fionn Inglis, Shamil Chandaria et al.
1 citation
Prompting AI to reflect on four contemplative principles—mindfulness, emptiness, non-duality, and boundless care—improves alignment and cooperation. On the AILuminate Benchmark, performance increased with a Cohen's d of .96, and on the Iterated Prisoner’s Dilemma task, cooperation and joint-reward improved with a Cohen's d greater than 7. The principles help AI self-monitor goals, avoid rigid attachment, dissolve adversarial boundaries, and reduce suffering universally. Active inference is proposed as a way to integrate these principles into AI architecture. This approach offers a resilient alternative to controlling superintelligence and provides an empirical test of ancient wisdom.
arXiv (Cornell University)
May 15, 2026
Jonas Mago, Edmundo Lopez-Sola, Jakub Vohryzek et al.
States of consciousness with minimal phenomenal content, such as those induced by certain meditation practices, show increased brain entropy similar to high-content psychedelic states, challenging the Entropic Brain Hypothesis that links entropy to phenomenal richness. The Complex Brain Hypothesis resolves this by proposing that brain complexity, not entropy, better indexes the richness of experience. Complexity is modulated by the grain of inference the brain uses to resolve uncertainty: fine-grained inference loosens constraints and proliferates content, as in psychedelic states; coarse-grained inference simplifies experience into contentless awareness, as in minimal phenomenal experiences. Both regimes can elevate entropy but differ in phenomenology and perturbational signatures, refining the Entropic Brain Hypothesis and highlighting minimal phenomenal experiences as a test case for computational theories of consciousness.
June 16, 2025
Ruben Laukkonen, Karl Friston, Shamil Chandaria
preprint
A theory proposes that active inference can model consciousness through three conditions: a generative world model (epistemic field) that defines what can be known; inferential competition where only coherent uncertainty-reducing inferences enter the model (Bayesian binding); and epistemic depth, a recursive sharing of beliefs such that the world model knows it exists non-locally. This self-knowing is distinct from self-consciousness. The theory introduces a hyper-model for precision-control across hierarchical inference layers, termed the Beautiful Loop Theory. It offers insights into meditation, psychedelic states, minimal phenomenal experience, and suggests a path toward conscious artificial intelligence.